Noise suppressors address the problem of unknown external sources adding noise to an audio signal of interest by attempting to reduce the noise.
Traditional noise suppressors use generic information when dealing with noise because, in general, noise can include one of many types of noise, which may have distinct spectral densities (e.g., white noise, Brownian noise, grey noise, etc.) and/or distinct probability distributions (e.g., Gaussian, Poisson, Cauchy, etc.). Traditional noise suppressors also estimate the level of noise in certain critical bands of frequencies, and based on these estimates, they also perform suppression of the entire critical bands.
In audio coding, an incoming, continuous audio signal may be converted into a predetermined number of discrete bits. As part of this conversion, quantization noise may be added into the audio signal. For example, many audio coders introduce noise in both time and frequency. The key difference between the noise introduced by audio coding compared to noise added by an unknown external source is that the former is added to certain frequency bands in a known way.
Components of a system may also introduce noise into a signal of interest. As with audio coding, the noise introduced by system components may also have deterministic characteristics. For example, electronic components may introduce noise centered around a particular frequency (e.g., noise generated by an electronic component, where the noise can vary, but is known to be concentrated in limited frequencies or limited frequency bands (e.g., based on the characteristics of the electronic component)).
Whenever a noise suppressor performs suppression on a primary signal, distortion of the primary signal can occur. Additionally, if a traditional noise suppressor performs suppression of an entire critical band or a wide band of frequencies, information may be lost. Accordingly, more effective strategies for dealing with undesirable noise components of an audio signal are needed.